A multiple-group hidden Markov model for multi-source data. Cross-country differences in employment mobility in the presence of measurement error

Roberta Varriale; Mauricio Garnier Villarreal; Dimitris Pavlopoulos; Danila Filipponi

A multiple-group hidden Markov model for multi-source data. Cross-country differences in employment mobility in the presence of measurement error

Roberta Varriale, Mauricio Garnier Villarreal, Dimitris Pavlopoulos, Danila Filipponi

Research output: Contribution to Journal › Article › Academic › peer-review

Abstract

In this paper, we develop a multigroup hidden Markov model to tackle the issue of measurement error in multi-source data from different countries. We focus, in particular, on the measurement of employment mobility in the Netherlands and Italy using linked data from the Labour Force Survey and administrative sources. The measurement error correction we apply with our model reconciles differences between data sources and shows that cross-country differences in employment mobility are smaller than originally thought. Error-corrected estimates indicate that mobility from temporary to permanent employment has become, over time, larger in Italy than in the Netherlands, while mobility from non-employment to temporary employment has steadily been higher in the Netherlands than in Italy.

Original language	English
Journal	Big Data Research
Publication status	Accepted/In press - 27 Mar 2025

Funding

This article is part of the “DYNANSE: Righting the Wrongs. A Life Course Dynamics Approach for Non-Standard Employment” project, which has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 864471).

Funders	Funder number
European Research Council ERC

Keywords

Register data
Measurement error
hidden Markov model
Latent class analysis
cross-country analysis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Persistent URL (handle)

Persistent link

DYNANSE: Righting the Wrongs, A Life Course Dynamics Approach for Non-Standard Employment (ERC Consolidator Grant project)
Pavlopoulos, D. (Principal Investigator), Garnier Villarreal, M. (Project Researcher), Bouwhuis, S. (Project Researcher), Csorba, I. (Project Researcher), Chowdhury, T. M. (PhD candidate), Eberlein, L. (PhD candidate), Pankowska, P. K. (Project Researcher), Pankowska, P. K. (Project Researcher), Eberlein, L. (Project Researcher), Chowdhury, T. M. (Project Researcher), Steenks, K. (Project Researcher) & Jiménez Henríquez, M. J. (Project Researcher)
1/09/20 → 28/02/26
Project: Research

Cite this

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title = "A multiple-group hidden Markov model for multi-source data. Cross-country differences in employment mobility in the presence of measurement error",

abstract = "In this paper, we develop a multigroup hidden Markov model to tackle the issue of measurement error in multi-source data from different countries. We focus, in particular, on the measurement of employment mobility in the Netherlands and Italy using linked data from the Labour Force Survey and administrative sources. The measurement error correction we apply with our model reconciles differences between data sources and shows that cross-country differences in employment mobility are smaller than originally thought. Error-corrected estimates indicate that mobility from temporary to permanent employment has become, over time, larger in Italy than in the Netherlands, while mobility from non-employment to temporary employment has steadily been higher in the Netherlands than in Italy.",

keywords = "Register data, Measurement error, hidden Markov model, Latent class analysis, cross-country analysis",

author = "Roberta Varriale and {Garnier Villarreal}, Mauricio and Dimitris Pavlopoulos and Danila Filipponi",

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journal = "Big Data Research",

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T1 - A multiple-group hidden Markov model for multi-source data. Cross-country differences in employment mobility in the presence of measurement error

AU - Varriale, Roberta

AU - Garnier Villarreal, Mauricio

AU - Pavlopoulos, Dimitris

AU - Filipponi, Danila

PY - 2025/3/27

Y1 - 2025/3/27

N2 - In this paper, we develop a multigroup hidden Markov model to tackle the issue of measurement error in multi-source data from different countries. We focus, in particular, on the measurement of employment mobility in the Netherlands and Italy using linked data from the Labour Force Survey and administrative sources. The measurement error correction we apply with our model reconciles differences between data sources and shows that cross-country differences in employment mobility are smaller than originally thought. Error-corrected estimates indicate that mobility from temporary to permanent employment has become, over time, larger in Italy than in the Netherlands, while mobility from non-employment to temporary employment has steadily been higher in the Netherlands than in Italy.

AB - In this paper, we develop a multigroup hidden Markov model to tackle the issue of measurement error in multi-source data from different countries. We focus, in particular, on the measurement of employment mobility in the Netherlands and Italy using linked data from the Labour Force Survey and administrative sources. The measurement error correction we apply with our model reconciles differences between data sources and shows that cross-country differences in employment mobility are smaller than originally thought. Error-corrected estimates indicate that mobility from temporary to permanent employment has become, over time, larger in Italy than in the Netherlands, while mobility from non-employment to temporary employment has steadily been higher in the Netherlands than in Italy.

KW - Register data

KW - Measurement error

KW - hidden Markov model

KW - Latent class analysis

KW - cross-country analysis

M3 - Article

SN - 2214-5796

JO - Big Data Research

JF - Big Data Research

ER -

A multiple-group hidden Markov model for multi-source data. Cross-country differences in employment mobility in the presence of measurement error

Abstract

Funding

Keywords

UN SDGs

Persistent URL (handle)

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Projects

DYNANSE: Righting the Wrongs, A Life Course Dynamics Approach for Non-Standard Employment (ERC Consolidator Grant project)

Cite this